CONVENERS:
Philipp Marr, University of Vienna, Austria
Thomas Glade, University of Vienna, Austria
Chris Massey, Institute of Geological and Nuclear Sciences Limited (GNS Science), New Zealand
Dalia Kirschbaum, United States Geological Survey, USA
Raymond W.M. Cheung, Geotechnical Engineering Office, Civil Engineering and Development Department, Hong Kong

Landslides constitute one of the major natural threats to human lives, settlements and infrastructure. To reduce the number of affected humans and the destruction caused by landslides, different approaches such as improved land-use-planning and risk awareness of the potentially affected communities are applied. Landslide monitoring is a key tool which increases our knowledge on landslide dynamics, and can be used to alert potentially affected persons and communities. In this context, experiences and examples from case studies provide a valuable source of information, e.g., for exploring innovative methodologies or in-depth examination of surface and subsurface dynamics of landslides.

Landslide monitoring plays an important role in gaining knowledge about fundamental landslide processes. Monitoring systems focus on changes on the surface or subsurface, or on the combination of both. The potential sensors range for different landslide types (e.g., rockfall, debris flow, translational or rotational landslide) and kinematics (fast rockfall up to a slow creeping deep seated failure). The monitoring methods cover all kinds of technologies, ranging from the application of traditional monitoring methods (e.g., inclinometers, extensometers, piezometers) to the improvement of new and advanced technologies, including remote sensing (e.g., InSAR, optical fibres) and geophysical methods.

The aim of this session is to explore the variety and experiences of methods applied within specific case studies. Contributions may focus on conceptual and methodological monitoring strategies, on different kind of sensors and analysis, thresholds and issued warnings., are particularly welcome. In this session the presentation of case studies is anticipated in various multi-temporal and spatial contexts. Experiences of landslide monitoring systems embedded in landslide early warning strategies are most welcome, as well as inter- and transdisciplinary innovative approaches, in particular at an experimental stage.

CONVENERS:
Christine Fey, Institute of Applied Geology, University of Natural Resources and Life Sciences, Vienna, Austria
Christina Rechberger, Institute of Applied Geology, University of Natural Resources and Life Sciences, Vienna, Austria
Chiara Crippa, Department of Earth and Environmental Science University of Milano-Bicocca, Milano, Italy
Louise Vick, Department of Geosciences, UiT The Arctic University of Norway, Tromsø, Norway

Landslides in remote and inaccessible terrain, such as mountainous regions, are challenging to investigate due to their complex evolutive style, the lack of monitoring data, and logistical difficulties in installing ground-based monitoring instrumentation. However, the investigation of landslides is important: i) to prevent cascade effects that can lead to long runouts and potentially endanger infrastructure, and ii) to understand the influence of changing boundary conditions due to climate change such as melting glaciers, permafrost degradation and changing in precipitation. Remote sensing data such as imagery, laser scanning and interferometric radar from terrestrial, airborne and spaceborne platforms offer a good possibility to analyze: i) the retrospective landslide development over a longer period, and ii) the current landslide deformation behavior. Data from different sensors and platforms differ in the temporal and spatial resolution. The spatial coverage, especially of deep-seated landslides, is very inhomogeneous and not all parts of the landslide are detected at the same time with the same resolution and with the same sensor. A compilation of different data is necessary and an uncertainty assessment for the interpretation of surface displacements is needed. In addition to these uncertainties, remote sensing data only provides information about surface change and no subsurface information. In this context, the interpretation of the derived surface deformation data together with geological field data and numerical modelling techniques is important to understand the subsurface mechanisms and processes of deep-seated landslides. This session focuses on integrated applications from remote sensing and their combination with landslide data from geological mapping, in situ measurements and numerical modelling methods aiming at exploiting all the information on geometry, kinematics and processes needed to understand the landslide past and present evolution. We encourage to discuss the data and method uncertainties and their consequences for geological interpretation.

CONVENERS:
Daniele Giordan, Research institute for Geo-Hydrological Protection, Italy
Lars Harald Blikra, The Norwegian Water Resources and Energy Directorate, Norway
Giovanni B. Crosta, Milano Biccocca University, Italy
Jean-Philippe Malet, Institut Terre et Environnement / University of Strasbourg, France
Nick J. Rosser, Durham University, United Kingdom

The recent evolution of landslide monitoring has been supported by the development of several technologies that allow the combined assessment of both ground surface and at depth displacements, as well as the acquisition of additional dynamic landslide parameters. Nowadays, complex monitoring networks are considered an essential element for the characterization of landslide kinematics and for the identification of critical conditions, which hold clues towards future risks. The importance of landslide monitoring networks is not limited to characterizing the movement of the gravitational process but is also vital for the calibration of numerical simulations, the analysis of future scenarios and the definition of thresholds. The latter is a crucial step for activating early warning procedures that are increasingly central for the management of risks related to landslide failure or collapse, particularly where there is potential for their interaction with infrastructure or assets. Adopting early warning procedures requires implementing and integrating a robust monitoring network that can often be hard to manage, both because of the quantity of generated data, and because of the complexity in carrying out a synthesis of the observations in order to build a reliable and usable conceptual landslide model. This session aims at collecting valuable experiences from case studies, where the deployment of a monitoring network supported a relevant understanding of the landslide evolution, its behavior and innovative procedures that consider, in particular: i) challenges and operative solutions for the use of complex landslide monitoring networks, ii) codified practices for the definition of thresholds, iii) innovative solutions for effective management of early warning procedures.

CONVENERS:
Guglielmo Rossi, Civil Protection Centre – University of Florence, Italy
Stratis Karantanellis, University of Michigan – Department of Earth and Environmental Sciences, USA
Lidia Loiotine, University of Lausanne – Faculty of Geosciences and Environment, Switzerland
Carlo Tacconi Stefanelli, University of Florence, Department of Erath Sciences, Italy
Francesco Mugnai, University of Florence Civil and Environmental Engineering Department, Italy
Marco Mulas, Università degli Studi di Modena e Reggio Emilia, Department of Chemical and Geological Sciences, Italy

Nowadays, active landslides can be identified and monitored with several platforms, ranging from terrestrial to crewed/uncrewed aerial vehicles or spaceborne satellites. Despite the imaging sensor adopted, the scientific community has extensive options in terms of image processing algorithms, which have been developed to detect changes and/or to derive spatially distributed displacements over time. The vast combinations of sensors and platforms, coupled with the significant range of geometric and temporal resolution, can lead to countless applications, such as UAV surveying and monitoring.

The combination of more sets of sensors and platforms integrated with ground truth datasets allows the development of solutions, methodologies and combination of techniques, such as photomonitoring. In this way different results, in the framework of landslide characterization and interpretation, can be achieved. These may include surface properties, glaciological processes, soil classification, displacement and morphological change detection and vegetation analysis.

This session will bring together the research community to present an overview of the recent innovations on airborne instruments, innovative processing algorithms, developments and observations for landslide characterization and monitoring.

CONVENERS:
Hans-Balder Havenith, University of Liege, Belgium
Veronica Pazzi, University of Trieste and University of Florence, Italy
Salvatore Martino, University of Rome (La Sapienza), Italy
Anne-Sophie Mreyen, University of Liege, Belgium
Romy Schlögel, Centre Spatial de Liege and University of Liege, Belgium

Investigation of landslides and monitoring for landslide detection are more and more approached by combining inputs from surface and subsurface data. The combination of close-range sensing and geophysical data provide a more complete view of active mass movements and landslide-prone contexts. Often these surveys are organized separately, and a full integration of surface and subsurface information is barely performed. Most data representations lack a digital environment that allows for a joint interpretation of remote sensing and geophysical data, also due to the different scales of related studies. 3D geomodelling can provide a better cross-validate surface and subsurface information. Geophysical data interpretation in particular can be affected by high levels of uncertainty; therefore, a well-integrated and jointly modelled surface and geophysical data will likely help reducing this uncertainty.

In addition, especially for large mass movements or group of investigated massive failures, the surface and subsurface models, even if well-constructed by integrating all processed inputs and outputs, are difficult to be analyzed and interpreted due to the complexity of information included in the models. Traditional maps and section views may not be sufficient to get a deeper insight into the landslide structure, and into the dynamics of failure processes. 3D geomodels generally provide a clearer overview of integrated datasets and, additionally, strongly support the construction of the input models for numerical simulations.

Finally, this session also covers the close-range sensing for detecting precursors of landslide occurrence as well as the monitoring of geophysical parameters changes within existing deforming masses, which produce valuable information on the ground deformation regime and help predict massive failures. Contributions presenting field lab results that combine both types of surface and subsurface imaging, as well as landslide process identification and surveying are particularly welcome.

CONVENERS:
Maria Teresa Brunetti, Istituto di Ricerca per la Protezione Idrogeologica, CNR, Perugia, Italy
Giriraj Amarnath, International Water Management Institute, Colombo, Sri Lanka
Silvia Peruccacci, Istituto di Ricerca per la Protezione Idrogeologica, CNR, Perugia, Italy
Thomas Stanley, University of Maryland, Baltimore, CO, USA
Luca Brocca, Istituto di Ricerca per la Protezione Idrogeologica, CNR, Perugia, Italy

Landslides are among the most dangerous natural hazards, particularly in developing countries with highly populated areas, where ground-based observations and data are lacking or incomplete.

In these areas, Earth observation (EO) data (e.g., topography, soil moisture, precipitation, vegetation) can be an important tool for the detection and monitoring of landslides and for their prediction both in space and in time.

The session is open to multidisciplinary contributions on:

• the use of EO in modelling landslide hazard, vulnerability and risk;
• the implementation of EO data in Landslide Early Warning Systems (LEWS);
• the development of validation procedures in the application of EO data in landslide prediction;
• current limits and future perspectives of EO products in landslide prediction
  Early-stage researchers are welcome to present their research.

CONVENERS:
Matteo Del Soldato, Earth Science Department of the University of Firenze, Firenze, Italy
Lorenzo Solari, European Environment Agency, Copenaghen, Denmark
Guadalupe Bru, Spanish National Survey (IGME-CSIC), Madrid
Qingkai Meng, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, China
Rosa Maria Palau Berasegui, Norwegian Geotechnical Institute (NGI), Oslo, Norway

Landslides have every year a huge socio-economic impact on the population, causing sometimes fatalities. The population growth induces uncontrolled urban expansion with new buildings on dormant or unknown landslides. There is a urgent demand for mapping and monitoring tools to support landslide management and mitigation strategies. Among the remote sensing observation techniques, Interferometric Synthetic Aperture Radar (InSAR) has become one of the most widely applied for landslide studies. InSAR data are now recognized as everyday ground motion measurement tools by a wide spectrum of users ranging from civil protection authorities to the industry.

Recently, the number of users accessing and analysing InSAR data has greatly increased thanks to the growing availability of free-to-use images, as the ones provided by Sentinel-1, or open processed products, as the ones coming from regional, national and continental ground motion services (e.g., the European Ground Motion Service or InSAR Norway). The development of open-source analysis software and the access to powerful cloud computing platforms have facilitated and sped up the InSAR analysis.

Authors are invited to present innovative approaches and methods based on InSAR analyses for landslides investigation, mapping and monitoring. Case studies involving the synergic use of in situ and remote sensing resources are particularly welcome, as well as applications showing the positive impact of InSAR data on civil protection practices. The research works shall cover a broad range of topics, which may include, but are not limited to:

• Mapping of landslides over wide areas;
• Near-real-time monitoring experiences;
• Monitoring of landslides by means of innovative InSAR approaches;
• Combination of InSAR and ground-based measurements;
• Single landslide characterization;
• integrating InSAR data and landslide models;
• Integration of InSAR data in landslide susceptibility model and impact evaluation;
• Local and regional scale applications for landslide post-event mapping;
• Definition of risk scenarios supported by InSAR data.

CONVENERS:
Massimiliano Bordoni, Department of Earth and Environmental Sciences, University of Pavia, Italy
Luca Ciabatta, Research Institute for Geo-Hydrological Protection, National Research Council, Italy
Evelina Volpe, Research Institute for Geo-Hydrological Protection, National Research Council, Italy
Thomas Stanley, University of Maryland, USA

Many regions worldwide are coping with the climatic global change, which is increasing the occurrence of extreme hydro-meteorological events. Landslides across a territory could increase significantly respect to actual and past scenarios, causing a modification of both the susceptibility of a region and the frequency of their triggering.

The use of techniques able to monitor and to predict these phenomena at different scales and in scarcely instrumented regions is fundamental. Soil moisture and rainfall measured through remote sensing can represent reliable and widespread data. For rainfall, state-of-the-art products cover decennial time series (e.g., Global Precipitation Measurement, EUMETSATH SAF). For soil moisture, different products can bring reliable measurements from a local/landscape to continental scales (e.g., SMMR, AMSR2, SMOS, SMAP, Metop/ASCAT, Sentinel 1). Innovative products, as soil moisture-derived rainfall, allow to retrieve rainfall from satellite soil moisture or integrating field measurements of precipitation. With enhanced spatial and temporal resolutions, satellite products could become a tool for early warning strategies for landslides, providing useful information in few hours.

This session aims to collect research concerning the most recent progress on use of soil moisture and rainfall data from remote sensing for monitoring and predicting landslides. We encourage presentations related to:

• inter-validation between land surface models, remote sensing approaches and in-situ monitoring;
• evaluation and trend analysis of soil moisture or rainfall satellite time series for monitoring landslides and for identifying triggering conditions;
• implementation of satellite measures of rainfall and soil moisture in physically-based or data-driven methods for the prediction of landslides;
• use of remote sensing products of soil moisture and rainfall in early warning system tools for landslides;
• use of remote sensing products for investigating the effects of climatic global changes on the susceptibility and hazards towards landslides.

CONVENERS:
Katsuo Sasahara, Kochi University, Japan
Michael Hendry, University of Alberta, Canada
Emanuele Intrieri, University of Florence, Italy
Renato Macciotta Pulisci, University of Alberta, Canada
David Elwood, University of Saskatchewan, Canada

The purpose of a landslide monitoring system is to provide reliable data for collecting in time the information regarding an unstable slope, to make a correct prediction of the kinematics and the evolutionary process. Among the wide set of parameters that can be possibly monitored, the most direct indicators of a slope stability are kinematic parameters, that is displacement and its velocity and acceleration.

For this reason, if the acquisition frequency is compatible with the evolution velocity of a landslide, the kinematic monitoring can be the base of early warning systems, which in many instances are the only (or at least the most cost-effective) possible countermeasures to reduce the risk represented by a collapse. Furthermore, acquisition frequencies for (near) real-time monitoring systems have transitioned to smaller time intervals. This allows an enhanced understanding of the response of a landslide to precipitation events, fluctuations in groundwater or water bodies at the toe of the slopes, anthropogenic activity (e.g., pit blasting operations, excavation), amongst others.

In the last 10 years significant developments have been made regarding displacement monitoring from spaceborne (for example in terms of revisit time), airborne (with the diffusion of drones) and ground-based sensors (new technologies such as micro electromechanical systems, wireless sensors networks, internet of things, improved radar techniques), other than continuous innovations concerning new software and algorithms (for example to cope with big data).

Such technological progresses generate more and higher quality data and better tools to interpret them, that in turn can lead to improve the understanding of landslides mechanics, to derive new methods to forecast the time of failure or to enhance the existing ones.

This session will welcome novel developments and practical applications of enhanced landslide displacement monitoring tools and approaches, as well as data analysis for improved understanding and forecasting of landslides with various scales from shallow landslides to deep-seated landslides, recent developments in technology, applications of early warning systems, and case histories applicable to the development of early warning systems. Papers may include (i) sensors and instruments, and/or (ii) data analysis and time to failure projections.

CONVENERS:
Samuele Segoni, University of Firenze, Italy
Manfred Stähli, Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
Neelima Satyam, Indian Institute of Technology Indore, Civil Engineering, India
Annette Patton, Sitka Sound Science Center, Sitka, AK, USA; University of Oregon, Eugene, OR, USA
Luca Piciullo, Norwegian Geotechnical Institute, Norway
Graziella Devoli, Norwegian Water Resources and Energy Directorate (NVE), Oslo, Norway

Landslide Early Warning Systems (LEWS) are operated worldwide to assess the imminent landslide danger and allow the timely evacuation of people at risk. LEWS can be applied at different spatial scales: operational and prototypal LEWS have been proposed and applied from slope to national scales. In addition to a competent and reliable operation, warning services face the great challenge of integrating new methods and technologies into proven systems to enhance their accuracy and performance. This session aims at discussing recent innovations to overcome current deficits in the successful operation of LEWS. Innovations may include new techniques for landslide detection, novel approaches and data for landslide forecasting, and enhanced technologies for ingesting and disseminating landslide information. This session addresses LEWS at all scales and stages of maturity. Test cases describing operational application of consolidated approaches are welcome, as well as works dealing with promising recent innovations, even if still at an experimental stage. The session will also welcome contributions highlighting how operators deal with the implementation of new methods that are promising but not yet established: a particular focus will be on the integration of innovations into established workflows and on the exploration of the trade-off between reliability and technical advancement. In addition, the session will cover all the LEWS aspects closely connected with social sciences, including communication strategies and the social perception of warnings and uncertainties. Ultimately, we would like to stimulate a discussion between developers of innovations and those who are in charge of making the best possible use of them. This is in the spirit of a recently founded international network, LandAware, with experts on LEWS from 48 countries worldwide.